Radiotherapy is used to treat cancers and other ailments in mammalian (e.g., human and animal) tissue. One such radiotherapy technique is a Gamma Knife, by which a patient is irradiated by a large number of low-intensity gamma rays that converge with high intensity and high precision at a target (e.g., a tumor). In another embodiment, radiotherapy is provided using a linear accelerator, whereby a tumor is irradiated by high-energy particles (e.g., electrons, protons, ions, and the like). The placement and dose of the radiation beam must be accurately controlled to ensure the tumor receives the prescribed radiation, and the placement of the beam should be such as to minimize damage to the surrounding healthy tissue, often called the organ(s) at risk (OARs). Radiation is termed “prescribed” because a physician orders a predefined amount of radiation to the tumor and surrounding organs similar to a prescription for medicine.
Traditionally, for each patient, a radiation therapy treatment plan (“treatment plan”) may be created using an optimization technique based on clinical and dosimetric objectives and constraints (e.g., the maximum, minimum, and mean doses of radiation to the tumor and critical organs). The treatment planning procedure may include using a three-dimensional image of the patient to identify a target region (e.g., the tumor) and to identify critical organs near the tumor. Creation of a treatment plan can be a time consuming process where a planner tries to comply with various treatment objectives or constraints (e.g., dose volume histogram (DVH) objectives), taking into account their individual importance (e.g., weighting) in order to produce a treatment plan that is clinically acceptable. This task can be a time-consuming trial-and-error process that is complicated by the various organs at risk (OARs) because as the number of OARs increases (e.g., up to thirteen for a head-and-neck treatment), so does the complexity of the process. OARs distant from a tumor may be easily spared from radiation, while OARs close to or overlapping a target tumor may be difficult to spare.
Computed Tomography (CT) imaging traditionally serves as the primary source of image data for treatment planning for radiation therapy. CT images offer accurate representation of patient geometry, and CT values can be directly converted to electron densities (e.g., Hounsfield units) for radiation dose calculation. However, using CT causes the patient to be exposed to additional radiation dosage. In addition to CT images, magnetic resonance imaging (MRI) scans can be used in radiation therapy due to their superior soft-tissue contrast, as compared to CT images. MRI is free of ionizing radiation and can be used to capture functional information of the human body, such as tissue metabolism and functionality.
Thus, MRI can be used to complement CT for more accurate structure contouring. However, MRI intensity values are not directly related to electron densities and cannot be directly used for dose computation; therefore, it is desirable to convert a MR image into a corresponding derived image, usually a CT image (often referred to as a “pseudo-CT image”). A pseudo-CT image, like a real CT image, has a set of data points that indicate CT values that are directly convertible to electron densities for radiation dose calculation. Thus, a pseudo-CT image derived from an MR image can be used to facilitate patient dose computation in radiation therapy treatment planning. Therefore, it is desirable to accurately generating a pseudo-CT image using MR image data in order for patients to be spared from additional radiation exposure arising from CT imaging. What is needed is for pseudo-CT images to be able to replace “real” CT images.
Typically, to create pseudo-CT images, atlas images are employed. An atlas image is a pre-existing image that is used as a reference to facilitate how a new image is to be translated to generate a derived image. For example, in the pseudo-CT image generation context, an atlas MR image and an atlas CT image can be used as references for generating a derived CT image from a new MR image. Atlas images can be previously generated of the same region of interest for the same patient who is the subject of the new MR images, where these atlas images have been analyzed to identify structures of interest. For example, in many treatment or diagnostic situations, the patient will need to be subjected to imaging at different times over the course of treatment or diagnosis. However, this need not always be true, for example, the atlas images do not need to be images of the same person.
The atlas MR image and the atlas CT image are preferably aligned with each other via a registration technique (i.e., such that an atlas MR image and an atlas CT image are “registered” with each other, or are in “registration”). With such registration, a give point in the atlas MR image for a particular location of the subject can be mapped to a given point in the atlas CT image for the same particular location (and vice versa). However, there may be a certain amount of error that can be present in this registration. As such, the registration between the atlas MR and the atlas CT may not be perfect.
In order to replace a real CT image, the pseudo-CT image should be as close as possible to a real CT image of the patient for the purpose of dose computation in radiation therapy treatment planning or for generating digitally reconstructed radiographs (DRRs) for image guidance. However, there is not a simple mathematical relationship between CT image intensity values (CT values) and MR intensity values. The difficulty arises because MR intensity values are not standardized and can vary significantly depending upon different MR scanner settings or different MR imaging sequence parameters. Thus, existing techniques, such as assigning CT values based on tissue segmentation of an MR image or those based on point comparison and weighted combination, provide only a very rough assignment, resulting in existing pseudo-CT images that lack the anatomical details of a true CT image.
Therefore, there is a need for generating pseudo-CT images with improved quality that are capable of replacing real CT images for the purposes of dose computation in treatment planning, generating digitally reconstructed radiographs (DRRs) for image guidance, and the like.